Dear all,
I am struggling with a (currently) cost-intensive problem: calculating the
(non-normalized) cumulative distribution function, given the (non-normalized)
probabilities. something like:
probs <- t(matrix(rep(1:100),nrow=10)) # matrix with row-wise probabilites
F <- t(apply(probs, 1, cumsum)) #SLOOOW!
One (already faster, but for sure not ideal) solution - thanks to Henrik
Bengtsson:
F <- matrix(0, nrow=nrow(probs), ncol=ncol(probs));
F[,1] <- probs[,1,drop=TRUE];
for (cc in 2:ncol(F)) {
F[,cc] <- F[,cc-1,drop=TRUE] + probs[,cc,drop=TRUE];
}
In my case, probs is a (30,000 x 10) matrix, and i need to iterate this step
around
200,000 times, so speed is crucial. I currently can make sure to have no NAs,
but in
order to extend matrixStats, this could be a nontrivial issue.
Any ideas for speeding up this - probably routine - task?
Thanks in advance,
Gregor
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